---
title: "Foster Care Project Presentation"
output: 
  flexdashboard::flex_dashboard:
    source_code: embed
    theme: spacelab
    social: [ "twitter", "facebook", "menu" ]
---

```{r message=FALSE, warning=FALSE, include=FALSE}
library(flexdashboard)
library(readxl)
library(tidyverse)
library(viridis)
library(plotly)
library(sf)
library(leaflet)
```

```{r load-data-transformation}
#national dataset
nation_data<-read_excel("data/national_afcars_trends_2009_through_2018.xlsx",sheet="Data")

#State dataset
#Numbers of Children Served in Foster Care, by State
state_served <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Served!A8:K60") %>%
gather(year,Served,'FY 2009':'FY 2018')

#Numbers of Children in Foster Care on September 30th, by State
state_inCare <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="In Care on September 30th!A8:K60") %>%
gather(year,InCare_Sep30,'FY 2009':'FY 2018')

#Numbers of Children Entering Foster Care, by State
state_entered <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Entered!A8:K60") %>%
gather(year,Entered,'FY 2009':'FY 2018')

#Numbers of Children Exiting Foster Care, by State
state_exited <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Exited!A8:K60") %>%
gather(year,Exited,'FY 2009':'FY 2018')

#Numbers of Children Waiting for Adoption, by State
state_waitingAdoption <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Waiting for Adoption!A8:K60") %>%
gather(year,Waiting_Adoption,'FY 2009':'FY 2018')

#Numbers of Children Waiting for Adoption Whose Parental Rights Have Been Terminated, by State
state_parentalRightsTerminated <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Parental Rights Terminated!A8:K60") %>%
gather(year,parental_rights_terminated,'FY 2009':'FY 2018')

#Numbers of Children Adopted, by State
state_adopted <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Adopted!A8:K60") %>%
gather(year,adopted,'FY 2009':'FY 2018')
```

```{r merge_data}
merge_cols<-c("State","year")
#The merge argument only takes two values as input, so you have to do them separately:
#state_df<- merge(state_served,state_inCare,state_entered,state_exited,state_waitingAdoption,state_parentalRightsTerminated,state_adopted,by=c("State","year"))

state_data<- merge(state_served,state_inCare,by=merge_cols)
state_data<- merge(state_data,state_entered,by=merge_cols)
state_data<- merge(state_data,state_exited,by=merge_cols)
state_data<- merge(state_data,state_waitingAdoption,by=merge_cols)
state_data<- merge(state_data,state_parentalRightsTerminated,by=merge_cols)
state_data<- merge(state_data,state_adopted,by=merge_cols)
```

```{r message=FALSE, warning=FALSE, include=FALSE}
us_states <- st_read("./shp/states.shp")
```

```{r}
state_data_2009 <- state_data %>% filter(year == 'FY 2009') %>% rename(STATE_NAME = State)
```

```{r message=FALSE, warning=FALSE}
us_states_mapped <- inner_join(us_states,state_data_2009,by="STATE_NAME")
```


How we selected the Best Map{.storyboard}
=========================================


### Initially we started with a Shape file, ggplot and ggplotly() function

```{r message=FALSE, warning=FALSE}
g <- us_states_mapped %>%
  ggplot() +
  geom_sf(aes(fill = Served,text = paste0("State: ",STATE_NAME,", Year : ", year))) +
  # coord_sf(crs = st_crs(102003)) +
  scale_fill_viridis("Served",begin = 0.06,end=0.95,option = "plasma") +
  ggtitle("Orphans Served by each state in 2009") +
  theme_minimal() +
  theme(legend.position = "bottom",
        axis.text.x=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.x=element_blank(),
        axis.ticks.y=element_blank(),
        panel.grid.major = element_blank())
ggplotly(g,tooltip = c("text","fill"))
```

***

- Points on visuals here

### We used 'usmap' package, ggplot and ggplotly() function


```{r}
state_data_2009 <- us_states_mapped %>% rename(state = STATE_NAME)

g1 <- usmap::plot_usmap(data = state_data_2009,values = "Served") +
  scale_fill_viridis("Served",begin = 0.06,end=0.8,option = "plasma") +
  ggtitle("Orphans Served by each state in 2009") +
  theme_minimal() +
  theme(legend.position = "bottom",
        legend.title=element_text(size=10), 
        legend.text=element_text(size=5))


ggplotly(g1)
```


***

Points here


### Used the Plotly package for chlopleth plots using plot_geo()

```{r message=FALSE, warning=FALSE}

#Set hover text
us_states_mapped$hover <- with(us_states_mapped,paste(STATE_NAME,'
',"Served: ", Served))

# give state boundaries a white border
l <- list(color = toRGB("white"), width = 2)

# specify some map projection/options
g2 <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

g3 <- plot_geo(us_states_mapped, locationmode = 'USA-states') %>%
  add_trace(
    z = ~Served, text = ~hover, locations = ~STATE_ABBR,
    color = ~Served, colors = viridis_pal(option = "D")(3)
  ) %>%
  colorbar(title = "Served") %>%
  layout(
    title = 'Orphans Served by each state in 2009
(Hover for breakdown)',
    geo = g2
  )

g3
```

***

My Points here

- Point 1

- Point 2

- Point 3



### Tried Leaflet

```{r}
popup1 <- paste0("US State Values",
                 "
State: ", 
                 us_states_mapped$STATE_NAME, 
                 "
Served: ", 
                 us_states_mapped$Served)

pal <- leaflet::colorFactor(viridis_pal(option = "D")(3), domain = us_states_mapped$Served)

g4 <- leaflet(us_states_mapped) %>% 
  addProviderTiles("OpenStreetMap.Mapnik") %>%
  addPolygons(data = us_states_mapped, 
              fillColor = ~pal(Served),
              fillOpacity = 0.9,       
              color = "darkgrey",      
              weight = 1.5,            
              popup = popup1)



g4
```

***

Points here

### Leaflet Legend Issue

```{r}
popup1 <- paste0("US State Values",
                 "
State: ", 
                 us_states_mapped$STATE_NAME, 
                 "
Served: ", 
                 us_states_mapped$Served)

pal <- leaflet::colorFactor(viridis_pal(option = "D")(3), domain = us_states_mapped$Served)

g5 <- leaflet(us_states_mapped) %>% 
  addProviderTiles("OpenStreetMap.Mapnik") %>%
  addPolygons(data = us_states_mapped, 
              fillColor = ~pal(Served),
              fillOpacity = 0.9,       
              color = "darkgrey",      
              weight = 1.5,            
              popup = popup1) %>%
  addLegend(pal = pal, values = ~Served, opacity = 0.7, title = NULL,
  position = "bottomright")


g5
```

***

Points here


### Final Plot we selected for Shiny

```{r message=FALSE, warning=FALSE}

#Set hover text
us_states_mapped$hover <- with(us_states_mapped,paste(STATE_NAME,'
',"Served: ", Served))

# give state boundaries a white border
l <- list(color = toRGB("white"), width = 2)

# specify some map projection/options
g6 <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

g7 <- plot_geo(us_states_mapped, locationmode = 'USA-states') %>%
  add_trace(
    z = ~Served, text = ~hover, locations = ~STATE_ABBR,
    color = ~Served, colors = viridis_pal(option = "D")(3)
  ) %>%
  colorbar(title = "Served") %>%
  layout(
    title = 'Orphans Served by each state in 2009
(Hover for breakdown)',
    geo = g6
  )

g7
```

***

My Points here

- Point 1

- Point 2

- Point 3




Tab 3 {.storyboard}
=========================================

### Tab 3 Slide 1

Visuals here

***

Points here

### Tab 3 Slide 2

Visuals Here

***

Points here

### Tab 3 Slide 3

Visuals here

***

Points here

### Tab 3 Slide 4

Visuals here

***

Points here



Conclusion {.storyboard}
=========================================

### THANK YOU!!

Thank You


***


Our GitHub repo: 


Our Rpubs link: 


Shiny app link:



THANK YOU!!